Special Issue Information

Dear Colleagues,

This special issue (SI) encourages to present research achievement of new theories and methods of signal processing the researchers develop. Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The application of digital computation to signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression. However, most of the challenges arising from digital signal processing in still need to be researched regardless of the application, because many research questions remain. Many limitations exist in various application environments, and the latest research to solve these problems is being studied by many researchers. We look forward to the latest research findings that suggest theories and practical solutions for various application based on Digital Signal Processing (DSP).

Authors are encouraged to submit contributions in any of the following or related areas for DSP:

Information theory based on DSP;

Algorithms based on DSP;

Real-time computing based on DSP;

Applications based on DSP;

Image & video processing;

Display technology based on DSP;

Machine learning based on DSP;

Data hiding & Watermarking;

Pattern recognition;

Learning mechanism.

Prof. Dr. Cheonshik KimGuest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.

Laser monitoring has received more and more attention in many application fields thanks to its essential advantages. The analysis shows that the target speech in the laser monitoring signals is often interfered by the echoes, resulting in a decline in speech intelligibility and
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Laser monitoring has received more and more attention in many application fields thanks to its essential advantages. The analysis shows that the target speech in the laser monitoring signals is often interfered by the echoes, resulting in a decline in speech intelligibility and quality, which in turn affects the identification of useful information. The cancellation of echoes in laser monitoring signals is not a trivial task. In this article, we formulate it as a simple but effective additive echo noise model and propose a cascade deep neural networks (C-DNNs) as the mapping function from the acoustic feature of noisy speech to the ratio mask of clean signal. To validate the feasibility and effectiveness of the proposed method, we investigated the effect of echo intensity, echo delay, and training target on the performance. We also compared the proposed C-DNNs to some traditional and newly emerging DNN-based supervised learning methods. Extensive experiments demonstrated the proposed method can greatly improve the speech intelligibility and speech quality of the echo-cancelled signals and outperform the comparison methods.
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Time-frequency (TF) signal features are widely used in specific emitter identification (SEI) which commonly arises in many applications, especially for radar signals. Due to data scale and algorithm complexity, it is difficult to obtain an informative representation for SEI with existing TF features.
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Time-frequency (TF) signal features are widely used in specific emitter identification (SEI) which commonly arises in many applications, especially for radar signals. Due to data scale and algorithm complexity, it is difficult to obtain an informative representation for SEI with existing TF features. In this paper, a feature extraction method is proposed based on synchrosqueezing transform (SST). The SST feature has an equivalent dimension to Fourier transform, and retains the most relevant information of the signal, leading to on average approximately 20 percent improvement in SEI for complex frequency modulation signals compared with existing handcrafted features. Numerous results demonstrate that the synchrosqueezing TF feature can offer a better recognition accuracy, especially for the signals with intricate time-varying information. Moreover, a linear relevance propagation algorithm is employed to attest to the SST feature importance from the perspective of deep learning.
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We present a new data hiding method based on Adaptive BTC Edge Quantization (ABTC-EQ) using an optimal pixel adjustment process (OPAP) to optimize two quantization levels. The reason we choose ABTC-EQ as a cover media is that it is superior to AMBTC in
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We present a new data hiding method based on Adaptive BTC Edge Quantization (ABTC-EQ) using an optimal pixel adjustment process (OPAP) to optimize two quantization levels. The reason we choose ABTC-EQ as a cover media is that it is superior to AMBTC in maintaining a high-quality image after encoding is executed. ABTC-EQ is represented by a form of trio(Q1,Q2,[Q3], BM) where Q is quantization levels (Q1≤Q2≤Q3), and BM is a bitmap). The number of quantization levels are two or three, depending on whether the cover image has an edge or not. Before embedding secret bits in every block, we categorize every block into smooth block or complex block by a threshold. In case a block size is 4x4, the sixteen secret bits are replaced by a bitmap of the smooth block for embedding a message directly. On the other hand, OPAP method conceals 1 bit into LSB and 2LSB respectively, and maintains the quality of an image as a way of minimizing the errors which occur in the embedding procedure. The sufficient experimental results demonsrate that the performance of our proposed scheme is satisfactory in terms of the embedding capacity and quality of an image.
Full article

Recently, the difference and sum (diff-sum) coarray has attracted much attention in one-dimensional direction-of-arrival estimation for its high degrees-of-freedom (DOFs). In this paper, we utilize both the spatial information and the temporal information to construct the diff-sum coarray for planar sparse arrays. The
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Recently, the difference and sum (diff-sum) coarray has attracted much attention in one-dimensional direction-of-arrival estimation for its high degrees-of-freedom (DOFs). In this paper, we utilize both the spatial information and the temporal information to construct the diff-sum coarray for planar sparse arrays. The diff-sum coarray contains both the difference coarray and the sum coarray, which provides much higher DOFs than the difference coarray alone. We take a planar coprime array consisting of two uniform square subarrays as the array model. To fully use the aperture-extending ability of the diff-sum coarray, we propose two novel configurations to improve the planar coprime array. The first configuration compresses the inter-element spacing of one subarray and results in a larger consecutive area in the coarray. The second configuration rearranges the two subarrays and introduces a proper separation between them, which can significantly reduce the redundancy of the diff-sum coarray and increase the DOFs. Besides, we derive the closed-form expressions of the central consecutive ranges in the coarrays of the proposed array configurations. Simulations verify the superiority of the proposed array configurations.
Full article

The improvement of resolution of digital video requires a continuous increase of computation invested into Frame Rate Up-Conversion (FRUC). In this paper, we combine the advantages of Edge-Preserved Filtering (EPF) and Bidirectional Motion Estimation (BME) in an attempt to reduce the computational complexity.
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The improvement of resolution of digital video requires a continuous increase of computation invested into Frame Rate Up-Conversion (FRUC). In this paper, we combine the advantages of Edge-Preserved Filtering (EPF) and Bidirectional Motion Estimation (BME) in an attempt to reduce the computational complexity. The inaccuracy of BME results from the existing similar structures in the texture regions, which can be avoided by using EPF to remove the texture details of video frames. EPF filters out by the high-frequency components, so each video frame can be subsampled before BME, at the same time, with the least accuracy degradation. EPF also preserves the edges, which prevents the deformation of object in the process of subsampling. Besides, we use predictive search to reduce the redundant search points according to the local smoothness of Motion Vector Field (MVF) to speed up BME. The experimental results show that the proposed FRUC algorithm brings good objective and subjective qualities of the interpolated frames with a low computational complexity.
Full article

In order to get rid of the dependence of the navigation and positioning system on the global navigation satellite system (GNSS), radio, television, satellite, and other signals of opportunity (SOPs) can be used to achieve receiver positioning. The space-based SOPs based on satellites
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In order to get rid of the dependence of the navigation and positioning system on the global navigation satellite system (GNSS), radio, television, satellite, and other signals of opportunity (SOPs) can be used to achieve receiver positioning. The space-based SOPs based on satellites offer better coverage and availability than ground-based SOPs. Based on the related research of Iridium SOPs positioning in the open environment, this paper mainly focuses on the occluded environment and studies the Iridium SOPs positioning technique in weak signal environment. A new quadratic square accumulating instantaneous Doppler estimation algorithm (QSA-IDE) is proposed after analysing the orbit and signal characteristics of the Iridium satellite. The new method can improve the ability of the Iridium weak signal Doppler estimation. The theoretical analysis and positioning results based on real signal data show that the positioning based on Iridium SOPs can be realized in a weak signal environment. The research broadens the applicable environment of the Iridium SOPs positioning, thereby improving the availability and continuity of its positioning.
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This manuscript firstly proposes a reduced size, low-complexity Angle of Arrival (AoA) approach, called Reduced Uniform Projection Matrix (RUPM). The RUPM method applies a Uniform Sampling Matrix (USM) criterion to sample certain columns from the obtained covariance matrix in order to efficiently find the directions of the incident signals on an antenna array. The USM methodology is applied to reduce the dependency between the adjacent sampled columns within a covariance matrix; then, the sampled matrix is used to construct the projection matrix. The size of the obtained projection matrix is reduced to minimise the computational complexity in the searching grid stage. A theoretical analysis is presented to demonstrate that the USM methodology can increase the Degrees of Freedom (DOFs) with the same aperture size and number of sampled columns compared to the classical sampling criterion. Then, a polynomial root is constructed as an alternative efficient computational solution of the UPM method in a one-dimensional (1D) array spectrum peak searching problem. It is found that this distribution increases the number of produced nulls and enhances noise immunity. The advantage of the RUPM method is that it is appropriate to apply for any array configuration while the Root-UPM offers better estimation accuracy with less execution time under a uniform linear array condition. A computer simulation based on various scenarios is performed to demonstrate the theoretical claims. The proposed direction-finding methods are compared with several AoA methods in terms of the required execution time, Signal-to-Noise Ratio (SNR) and different numbers of data measurements. The results verify that the new methods can achieve significantly better performance with reduced computational demands.
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The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic
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The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.
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